Effects of Measurement Height and Low-Pass-Filtering Corrections on Eddy-Covariance Flux Measurements Over a Forest Clearing with Complex Vegetation

Abstract

Flux measurements over heterogeneous surfaces with growing vegetation and a limited fetch are a difficult task, as measurement heights that are too high or too low above the canopy adversely affect results. The aim of this study is to assess implications from measurement height in regard to low-pass filtering, footprint representativeness, and energy balance closure for a clear-cut site with regrowing vegetation of varying height. For this, measurements from two open-path eddy-covariance systems at different heights are compared over the course of one growing season. Particular attention is paid to low-pass-filtering corrections, for which five different methods are compared. Results indicate significant differences between fluxes from the upper and lower systems, which likely result from footprint differences and an insufficient spectral correction for the lower system. Different low-pass-filtering corrections add an uncertainty of 3.4% (7.0%) to CO2 fluxes and 1.4% (3.0%) to H2O fluxes for the upper (lower) system, also leading to considerable differences in cumulative fluxes. Despite limitations in the analysis, which include the difficulty of applying a footprint model at this study site and the likely influence of advection on the total exchange, the analysis confirms that information about the choice of spectral correction method and measurement-height changes are critical for interpreting data at complex sites.

Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry

“…The results showed that the relative effect of the spectral correction method on the flux magnitude increases with higher spectral correction factors, or in other words, with an increasing loss of high-frequency signal. When the relative importance of the spectral correction method increases, systematic small differences between spectral correction methods are added up and the difference between spectral methods and the total uncertainty of the flux increases (Mauder and Foken, 2006;Reitz et al, 2022). As the LC-EC per definition has stronger loss of high-frequency signal, applying the right spectral correction method is more important compared to CON-EC.…”

“…Based on our results, and especially the better energy balance closure and energy balance ratio, the Horst (1997) method was chosen as preferred spectral correction method in the current study, even though the Ibrom et al (2007) method was designed for closed path EC setups. As the LC-EC fluxes where still deviating from the CON-EC, it would be interesting to test a wide variety of other spectral correction methods in the future, especially because the system design of the LC-EC is different from CON-EC setups which have been used and thoroughly tested in the past (Polonik et al, 2019;Reitz et al, 2022).…”

“…Moreover, not measuring the storage components (soil, air and biomass) of the energy balance at the agroforestry site, might give a biased image of the EBC, as the tree strips could potentially store energy. When a EC tower is taller, the high-frequency eddies become less important and the cospectrum peak and the energycontaining eddies shift to lower frequencies, and opposite, closer to the ground the higher frequency eddies are more important (Moncrieff et al, 1997;Reitz et al, 2022). This effect was clearly seen when the high-frequency spectral correction factors of the 10 m tall agroforestry tower were compared to the 3 m tall grassland tower (Figure 10).…”

“…The energy balance closure (EBC) for each EC system was assessed as an additional indicator for data quality. In the current study we used the energy balance closure as described in Equation 1, similar to Mauder and Foken (2006) and Reitz et al (2022).…”

“…molecules inside the tubing (Massman, 1991;Ibrom et al, 2007;Polonik et al, 2019). Furthermore, there are many different spectral correction methods available, each with their own assumptions, which is an additional source of uncertainty in itself (Polonik et al, 2019;Reitz et al, 2022;Emad, 2023).…”